NCP-GENL Model Optimization Practice Question
Network Topology
Refer to the exhibit. The engineer is attempting to deploy on an NVIDIA Orin platform but encounters a runtime error. What is the most likely cause of the failure?
⚠ Common exam trap
Candidates often assume the error is a general memory or driver issue, failing to check if the specific operations in the model graph are actually supported by the DLA hardware architecture.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Unsupported operators are being targeted for the DLA
The inclusion of '--dla 0' forces the engine to run on the Deep Learning Accelerator (DLA) core. Many complex LLM operations, such as specific activation functions or advanced attention mechanisms, are not supported by the DLA's fixed-function logic. If the model graph contains unsupported operators, the build will either fail or generate a non-functional plan, as the DLA has a more restricted operator set than the primary GPU cores.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The workspace memory is too large
Why it's wrong here
A workspace of 4096 MB is generally sufficient for most models. The error is unlikely to be related to the size of the workspace, as Orin platforms typically have enough memory to accommodate this allocation. The issue stems from the compatibility of the operators with the DLA architecture.
- ✗
The DLA hardware is not enabled on this device
Why it's wrong here
The command correctly identifies the DLA, suggesting the hardware is present. The issue is not the existence of the DLA, but rather the capability of the DLA to execute the specific operations contained within the model graph. Many LLM operators are incompatible with DLA fixed-function hardware.
- ✓
Unsupported operators are being targeted for the DLA
Why this is correct
The DLA is a specialized hardware accelerator with limited operator support compared to the GPU. LLMs often use complex or custom operations that the DLA cannot execute. Forcing these operations onto the DLA via the CLI flags will cause the builder to fail because it cannot map the graph.
- ✗
FP16 precision is not supported on DLA
Why it's wrong here
DLA hardware is specifically designed to support FP16 and INT8 operations. The problem is not the precision of the weights, but the structural complexity of the model operations themselves. The operator set, not the precision, is the limiting factor for DLA compatibility in this scenario.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
This NCP-GENL practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCP-GENL exam.